






class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     {}


     def 					_A							(					self							:						str   ):
       print(self.vertex   )
       for i in self.vertex:
         print(UpperCAmelCase_       ,			" -> "       ,			" -> ".join([str(UpperCAmelCase_   ) for j in self.vertex[i]]   )   )


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						int   ):
       # check if vertex is already present,
       if from_vertex in self.vertex:
         self.vertex[from_vertex].append(UpperCAmelCase_   )
       else:
         # else make a new vertex
         SCREAMING_SNAKE_CASE							:   List[str]						    =     [to_vertex]


     def 					_A							(					self							:						int   ):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE							:   Any						    =     [False] * len(self.vertex   )

       # call the recursive helper function
       for i in range(len(self.vertex   )   ):
         if not visited[i]:
           self.dfs_recursive(UpperCAmelCase_       ,			UpperCAmelCase_   )






     def 					_A							(					self							:						Dict       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						list   ):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE							:   Any						    =     True

       print(UpperCAmelCase_       ,			end=" "   )

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
         if not visited[i]:
           self.dfs_recursive(UpperCAmelCase_       ,			UpperCAmelCase_   )


if __name__ == "__main__":
   snake_case     					=		Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print("""DFS:""")
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






